Research efforts on 3DTV technology have been strengthened worldwide recently, covering the whole media processing chain from capture to display. Different 3DTV systems rely on different 3D scene representations that integrate various types of data. Efficient coding of these data is crucial for the success of 3DTV. Compression of pixel-type data including stereo video, multiview video, and associated depth or disparity maps extends available principles of classical video coding. Powerful algorithms and open international standards for multiview video coding and coding of video plus depth data are available and under development, which will provide the basis for introduction of various 3DTV systems and services in the near future. Compression of 3D mesh models has also reached a high level of maturity. For static geometry, a variety of powerful algorithms are available to efficiently compress vertices and connectivity. Compression of dynamic 3D geometry is currently a more active field of research. Temporal prediction is an important mechanism to remove redundancy from animated 3D mesh sequences. Error resilience is important for transmission of data over error prone channels, and multiple description coding (MDC) is a suitable way to protect data. MDC of still images and 2D video has already been widely studied, whereas multiview video and 3D meshes have been addressed only recently. Intellectual property protection of 3D data by watermarking is a pioneering research area as well. The 3D watermarking methods in the literature are classified into three groups, considering the dimensions of the main components of scene representations and the resulting components after applying the algorithm. In general, 3DTV coding technology is maturating. Systems and services may enter the market in the near future. However, the research area is relatively young compared to coding of other types of media. Therefore, there is still a lot of room for improvement and new development of algorithms.

The prompt and adequate detection of abnormal cardiac conditions by computer-assisted long-term monitoring systems depends greatly on the reliability of the implemented ECG automatic analysis technique, which has to discriminate between different types of heartbeats. In this paper, we present a comparative study of the heartbeat classification abilities of two techniques for extraction of characteristic heartbeat features from the ECG: (i) QRS pattern recognition method for computation of a large collection of morphological QRS descriptors; (ii) Matching Pursuits algorithm for calculation of expansion coefficients, which represent the time-frequency correlation of the heartbeats with extracted learning basic waveforms. The Kth nearest neighbour classification rule has been applied for assessment of the performances of the two ECG feature sets with the MIT-BIH arrhythmia database for QRS classification in five heartbeat types (normal beats, left and right bundle branch blocks, premature ventricular contractions and paced beats), as well as with five learning datasets-one general learning set (GLS, containing 424 heartbeats) and four local sets (GLS+about 0.5, 3, 6, 12 min from the beginning of the ECG recording). The achieved accuracies by the two methods are sufficiently high and do not show significant differences. Although the GLS was selected to comprise almost all types of appearing heartbeat waveforms in each file, the guaranteed accuracy (sensitivity between 90.7% and 99%, specificity between 95.5% and 99.9%) was reasonably improved when including patient-specific local learning set (sensitivity between 94.8% and 99.9%, specificity between 98.6% and 99.9%), with optimal size found to be about 3 min. The repeating waveforms, like normal beats, blocks, paced beats are better classified by the Matching Pursuits time-frequency descriptors, while the wide variety of bizarre premature ventricular contractions are better recognized by the morphological descriptors.

In this paper, we propose two novel methods for robot-world-hand–eye calibration and provide a comparative analysis against six state-of-the-art methods. We examine the calibration problem from two alternative geometrical interpretations, called ‘hand–eye’ and ‘robot-world-hand–eye’, respectively. The study analyses the effects of specifying the objective function as pose error or reprojection error minimization problem. We provide three real and three simulated datasets with rendered images as part of the study. In addition, we propose a robotic arm error modeling approach to be used along with the simulated datasets for generating a realistic response. The tests on simulated data are performed in both ideal cases and with pseudo-realistic robotic arm pose and visual noise. Our methods show significant improvement and robustness on many metrics in various scenarios compared to state-of-the-art methods.

We suggest a compound full-reference stereo-video quality metric composed of two components: a monoscopic quality component and stereoscopic quality component. While the former assesses the trivial monoscopic perceived distortions caused by blur, noise, contrast change etc., the latter assesses the perceived degradation of binocular depth cues only. We use the structural similarity index as a measure for perceptual similarity and design a multiscale algorithm for obtaining a perceptual disparity map and a stereo-similarity map to be used in the suggested metric. We verify the performance of the metric with subjective tests on distorted stereo images and coded stereo-video sequences with a final aim to build a perceptually-aware feedback for a H.264 based stereo- video encoder.

In this paper, we present a novel stereoscopic video quality assessment method based on 3D-DCT transform. In our approach, similar blocks from left and right views of stereoscopic video frames are found by block-matching, grouped into 3D stack and then analyzed by 3D-DCT. Comparison between reference and distorted images are made in terms of MSE calculated within the 3D-DCT domain and modified to reflect the contrast sensitive function and luminance masking. We validate our quality assessment method using test videos annotated with results from subjective tests. The results show that the proposed algorithm outperforms current popular metrics over a wide range of distortion levels.

Depth of field is an important factor of imaging systems that highly affects the quality of the acquired spatial information. Extended depth of field (EDoF) imaging is a challenging ill-posed problem and has been extensively addressed in the literature. We propose a computational imaging approach for EDoF, where we employ wavefront coding via a diffractive optical element (DOE) and we achieve deblurring through a convolutional neural network. Thanks to the end-to-end differentiable modeling of optical image formation and computational post-processing, we jointly optimize the optical design, i.e., DOE, and the deblurring through standard gradient descent methods. Based on the properties of the underlying refractive lens and the desired EDoF range, we provide an analytical expression for the search space of the DOE, which is instrumental in the convergence of the end-to-end network. We achieve superior EDoF imaging performance compared to the state of the art, where we demonstrate results with minimal artifacts in various scenarios, including deep 3D scenes and broadband imaging.